CN104093618A

CN104093618A - Apparatus, system and method for risk indicator calculation for driving behaviour and for reconstructing a vehicle trajectory

Info

Publication number: CN104093618A
Application number: CN201380005381.6A
Authority: CN
Inventors: 斯尔詹·塔迪奇; 巴米兰·沃卡洛维克
Original assignee: Impulse Function F6 Co Ltd
Current assignee: Impulse Function F6 Co Ltd
Priority date: 2012-01-13
Filing date: 2013-01-14
Publication date: 2014-10-08
Also published as: JP2015513131A; US20140358840A1; EP2802498A1; KR20140119119A; CA2863229A1; KR20140121845A; JP2015513330A; AU2013208896A1; ZA201405166B; ZA201405155B; WO2013105869A1; HK1203910A1; BR112014017228A2; BR112014017243A2; US20150246654A1; CN104054118A; EP2803060A1; AU2012364960A1; CA2863098A1; HK1204132A1

Abstract

A first aspect relates to an apparatus, system and method for calculating a driving behaviour risk indicator for a driver of a vehicle. Said aspect involves obtaining a count of events occurring in each of a plurality of predetermined categories based on inputs from an inertial unit mounted on the vehicle, the inertial unit including a 3D inertial sensor with 3D gyroscope functionality, each event being indicative of at least one of dangerous and aggressive driving; and calculating a driving behaviour risk indicator based on the number of events in each category. According to a second aspect, an apparatus and method for reconstructing a vehicle trajectory is provided. Said aspect includes updating a sensor error model.

Description

Equipment, system and method that driving behavior risk indicator is calculated

Technical field

The present invention relates to the index of analyzing driving behavior and a kind of driving behavior inherent risk being provided.

Background technology

Different chaufeurs show different behaviors when driving.Some chaufeurs have more provocative than other, in their some driving behaviors, can emit larger risk.Hope provides feedback information to the driving behavior risk of chaufeur, like this, to them, can take corrective measure and change their driving behavior.

In order to determine vehicle location and the satellite navigation system of navigation is provided, be known.The use of the accident detector that comprises accelerometer is also known, as the information techenology unit in vehicle.These information techenology unit generally comprise mobile phone wireless electricity transceiver, and mobile phone wireless electricity transceiver is for transmitting the information between information of vehicles technical unit and teleprocessing entity.

In addition, the device that calculates vehicle driver's risk indicator is known, this device is used accelerometer to detect the acceleration of vehicle and the numerical value of deceleration, uses GPS to detect position and the speed of vehicle, uses GSM/GPRS unit to send and detects data to the risk indicator of processing center calculation vehicle driver.

Yet such system is relatively simple and crude, therefore, risk indicator can only be similar to.In addition, it depends on GPS and relies on GPS and sends data to remote location, and therefore, it is not self-sustaining.

Summary of the invention

The defect that the present invention is directed to prior art provides one for calculating the accurate equipment of driving behavior risk indicator, and it can be self-sustaining on vehicle, or the dispersing apparatus of inclusion information technical unit and teleprocessing entity, and described information techenology unit is on vehicle.

According to a first aspect of the invention, be provided for calculating the accurate equipment of vehicle driver's driving behavior risk indicator, comprise:

One processing and control element (PCE); And

One memory device;

This equipment is applicable to:

Comprise event count, described event occurs in each of a plurality of pre-set categories, the input of institute's event count based on being arranged on the inertance element on vehicle, described inertance element comprises the 3D inertial sensor with 3D spinfunction, and each event is dangerous driving at least one times and provokes the index of driving; And

In event number based on each classification, calculate driving behavior risk indicator.

Preferably, corresponding weight factor is stored in the memory device of each classification, and described equipment is suitable for calculating driving behavior risk indicator by corresponding weight factor being applied to each classification event number.

Preferably, the event number of described equipment based on occurring in predetermined period, is suitable for calculating driving behavior risk indicator.

Described equipment can be conducive to the time length based on predetermined period, is suitable for calculating driving behavior Risk Calculation.

Described equipment also can be conducive to the driving distance based on predetermined period, is suitable for calculating driving behavior risk indicator.

Preferably, described equipment, based on following reason, is suitable for calculating described driving behavior risk indicator:

Comprise the event number that occurs in the predetermined period in each classification;

Corresponding weight factor is applied to the event number of the predetermined period that occurs in each classification;

The event weights factor of all categories is added and to obtain the accumulative risk of predetermined period;

Determine that vehicle is in the driving distance of predetermined period; And

By driving distance, distinguish accumulative risk.

Preferably, the time of described equipment based on lasting, be suitable for revising the accumulative risk of predetermined period.

Preferably, described equipment further, based on environmental data, is suitable for revising driving behavior risk indicator.

In this example, described environmental data comprises road condition data, temperature data, at least one in weather data and geographic position data around.

Preferably, described pre-set categories comprises sharply turning, oversteer and evades two or more arbitrarily in manipulation.

Preferably, described equipment can be arranged in vehicle.

In this example, described equipment can comprise described inertance element, and preferably, further, in order to be communicated with teleprocessing entity, comprises projector and receptor.

As a kind of selection, described equipment can remote control on vehicle;

On vehicle, detect the event of each classification;

Described equipment is suitable for obtaining following at least one the quantity from vehicle:

The data of relevant each event, and

The event number of each classification.

As a kind of selection, described equipment can remote control on vehicle; And by accepting and process the data of the inertance element input being arranged on vehicle, be suitable for obtaining described quantity.

In these examples, preferably, be suitable for obtaining the event number of each vehicle of a plurality of different vehicle kinds in fleet, described event occurs in each in a plurality of pre-set categories, and is suitable for determining the driving behavior risk indicator of fleet.

In this example, preferably, described equipment further, is suitable for fleet's driving behavior risk indicator and alternatively, and at least one in the relative driving behavior risk indicator of acquisition be single car driving behavior risk indicator relatively.

Another aspect of the present invention, further, provides a system, and described system comprises a plurality of information techenology unit and remote processing units that are arranged on corresponding vehicle, wherein:

Described information techenology unit comprises inertial sensor unit;

Input data based on inertial sensor unit, at least one in described teleprocessing entity and described information techenology unit is suitable for the quantity of the event that obtains, described event occurs in each in a plurality of pre-set categories, and each event of described event represents dangerous driving and provokes at least one in driving; And

Event number based on each classification, described teleprocessing entity is suitable for calculating the driving behavior risk indicator of a plurality of information techenology unit associations.

Preferably, described inertial sensor unit comprises the 3D inertial sensor with 3D spinfunction.

Preferably, at least one teleprocessing entity and each information techenology unit are suitable for calculating driving behavior risk indicator.

Another aspect of the present invention provides a kind of method of the vehicle driver's of calculating driving behavior risk indicator, comprising:

Input data based on being arranged on the inertance element on vehicle, detect each the event occur in a plurality of pre-set categories, and each event represents dangerous driving and provokes at least one in driving;

Event number based on each classification, calculates driving behavior risk indicator.

Further, it is the method that many vehicles are determined driving behavior risk indicator that another aspect of the present invention provides a kind of, and described method comprises:

A plurality of preferred features of these methods are similar to the preferred feature of described equipment and system.

Further, the present invention on the other hand, provides a kind of equipment in the use of again planning on vehicle route, and described equipment comprises: processing and control element (PCE); And

Memory device,

Described equipment is suitable:

Store the data setting of the first predeterminable event, each data setting is included in corresponding the first Preset Time from being arranged on the data of the inertance element output vehicle, and described inertance element comprises the 3D inertial sensor with 3D spinfunction;

Based on external data and a plurality of sensor error models of storage, at the second Preset Time, upgrade sensor error model data;

Detection event; And

Storage sensor error model based on being stored in the major part nearest information of described event before starting, after event starts, upgrades and starts each the data setting to the 3rd Preset Time from described event;

Preferably, described equipment further, is suitable for again planning vehicle route on the basis of more new data setting.

Preferably, described event is car accident.

Preferably, wherein, the 3rd described Preset Time be defined as fixed cycle after one of them event occurs and from the signal of inertance element output still lower than fixed cycle of preset value.

Preferably, wherein, data are arranged in the first Preset Time and are stored in described wave band, and described wave band is adjusted after detection accident.

Preferably, described equipment further, is suitable for based on external data, after event occurs, determining the dead position data of vehicle, and the data based on upgrading arrange programme path again, using definite final position as starting point.

Preferably, described stationary vehicle position data comprises the posture of vehicle and at least one in satellite location data.

Preferably, described equipment further, is suitable for:

Determine the mean place after calculating, at least one of the fixing cycle use average final heading that more new data determine of the average acceleration vector calculating after accident being detected; With

Data based on upgrading, plan vehicle route again.

Preferably, described equipment further, is suitable for:

At least one in the final inclination that measure and calculation goes out, the final rotation calculating and the final irrelevance that calculates, this is to use the data that are stored in the renewal between the fixed cycle to calculate; And

Based on measurement result again programme path.

Preferably, described equipment is suitable for multiple the first predetermined period, after the storage data setting of use upgrading, by calculating at least one programme path again in vehicle location, speed and posture.

Preferably, described equipment further, is suitable for, before upgrading described sensor error model setting, calculating the inertial data setting of upgrading.

The present invention further, provides a kind of method of again planning vehicle route, and described method comprises:

Detection event;

The storage sensor error model of the nearest data before starting based on the described event of storage, after described event starts, upgrades from described event and starts to each data setting of the 3rd predeterminable event storage,

Storage sensor error model based on being stored in the major part nearest information of described event before starting, after event starts, upgrades and starts each the data setting to the 3rd Preset Time from described event; And

Data setting based on described renewal, plans vehicle route again.

A large amount of preferred features of these methods are similar to the preferred feature of front institute equipment and system.

Accompanying drawing explanation

By passing through further example and relevant accompanying drawing, the present invention will be further described now, wherein:

Fig. 1 is the schematic diagram that the present invention is suitable for an information techenology unit of use;

Fig. 2 is the schematic diagram that the present invention is suitable for a system of use;

Fig. 3 is the further schematic diagram that the present invention is suitable for the information techenology of use;

Fig. 4 is the schematic diagram with the 3D inertial sensor of 3D spinfunction;

Fig. 5 is the diagram of circuit of determining anxious accelerated events;

Fig. 6 is the diagram of circuit of determining sudden stop event;

Fig. 7 is the diagram of circuit of determining sharply turning event;

Fig. 8 is the diagram of circuit of determining oversteer event;

Fig. 9 determines the diagram of circuit of hiding event;

Figure 10 is the diagram of circuit of determining velocity variations event;

Figure 11 is the diagram of circuit of determining the driving event of not being interrupted the long time;

Figure 12 is the simplified diagram that the driving behavior risk indicator of described vehicle is calculated;

Figure 13 is the more detailed schematic diagram that the driving behavior risk indicator of vehicle is calculated;

Figure 14 is the schematic diagram that the present invention includes the system of fleet;

Figure 15 is the simplified diagram of calculating the driving behavior risk indicator calculating of vehicle fleet;

Figure 16 is the more detailed schematic diagram that calculates the driving behavior risk indicator calculating of vehicle fleet;

Figure 17 is the diagram of circuit of determining non-serious collision event;

Figure 18 is the diagram of circuit of determining serious collision event;

Figure 19 is the time schedule of again planning the collision that vehicle route is useful to explaining;

Figure 20 is the diagram of circuit of revising the output of described inertial sensor unit;

Figure 21 is the diagram of circuit of determining vehicle route;

Figure 22 is the diagram of circuit of running data recording.

The specific embodiment

The invention provides a kind of equipment, the system and method that calculate driving behavior Fengxian index.

First embodiment of the present invention comprises an information techenology unit 1000, and as shown in Figure 1, it can be arranged on (not shown) in vehicle.As shown in Figure 1, described unit 1000(represents with T-Box 1000) comprise three parts: 100,6 degree of freedom inertance elements 200 of core and selectable unit (SU) 310-369.Described core 100 forms the key component of the information techenology unit 1000 of the present embodiment together with described inertance element 200.

Can select to install one or more information techenology unit 1000 is arranged in vehicle.(meaning is after car load is assembled completely) can be installed in information techenology unit 1000 when the link of the rear market of vehicle, or can in group leader's process, be assemblied in vehicle.Described information techenology unit 1000 is connected power supply with vehicle DC, can but do not need to control and to be connected with disposal system with vehicle.

The described core 100 of described information techenology unit 1000 comprises GPS receiver 110, long apart from radio transceiver 120 and processing and control element (PCE) 130.GPS receiver 110 receiving satellite signals are with the position of computing information technical unit 1000, and this process is used satellite system, and for example GPS, Galileo, global navigation satellite system (GLONASS), compass, accurate zenith satellite system (QZSS) also can comprise concrete accurate enhancing function.Described total position can come from the information consolidation of different global position systems.Described receiving system 110 can complete in described 1000 inside, information techenology unit, the locating data that can provide by module (geographic coordinates) or by providing signal to complete to processing unit 130, except other standalone feature, they can compute location data.Global location is accepted system 110 and can be completed by a plurality of technology, and can use antenna assemblies and or exterior antenna.This exterior antenna can be placed on the inside (outside of described global location receiving system module 110) of disclosed described information techenology unit 1000 or the outside of disclosed described information techenology unit 1000.

Long apart from radio transceiver 120, comprise and receive and the function (comprising original data and/or sound signal and/or vision signal) of transmission data, described data are with or without compression, and with intrinsic that force and the password of selecting to add.Usually, length by one or more communication systems, is used mobile phone (mobile communications network) to connect apart from radio transceiver 120:

A) two generations (2G) GSMs (GSM, GPRS)

B) in 2.5 generations (2.5G), (EDGE)

C) 3 generations (3G) (UMTS, WBCDMA, HDCPA)

D) in 4 generations (4G), (LTE)

And/or system, as WiMax, and/or satellite communication system, and/or other data transmission radio system.

Described global location receiving system 110 and described length can, optionally in described information techenology unit 1000, complete and use as single module apart from radio transceiver 120.

Processing and control element (PCE) 130 is completed by any of a plurality of known CPU, preferably, by one 32 central process units, alternatively, has combined with DSP.

Described cpu central processing unit can not used or use arbitrary operating system (OS), for example, and the operating system based on Linux, microsoft operation system or other form, for example RTOS, VX Works and Android.The Linux scheme preferably embedding.

6 degree of freedom inertance elements 200 are the 3D inertial sensors with 3D spinfunction.Preferably, comprise 3D MEMS accelerometer 210 and 3D MEMS gyroscope 220.3D MEMS accelerometer 210 can be based on MEMS accelerometer sensor, by using one chip, a plurality of chip (usually, each direction, chip of each axle) or module to realize completely.3D MEMS gyroscope 220 can be based on MEMS technology, by using one chip, a plurality of chip (usually, each direction, chip of each axle) or module to realize completely.By MEMS technology (micro motor-driven mechanical sensor) or NEMS(nano-electric mechanical pick-up device) equipment that completes uses, and guaranteed that like this equipment size is little, lightweight and assembling is simple, and group shape is target information technical unit 1000PCB assembling.Described 3D MEMS accelerometer 210 and 3D MEMS gyroscope 220 can be used as one chip or single module provides.

Memory device 310 can be used arbitrary suitable technology to complete, and optionally, can become a part for the memory device of processing and control element (PCE) 130.Preferably, described memory device 310 comprises nonvolatile memory, design and the multiple correlation factors of stores processor and control unit 130 therein, and ceremony memory device can provide working storage for processing and control element (PCE) 130.Described memory device 310 is one or more following Resource Supply storages:

Data before transmission long-range wireless transceiver 12

Identify the data of vehicle

Entry data, service data and service data

Business processing related data

The associated vehicle of mount message technical unit 1000 is driven event data therein

Need to detect and reply the event data general introduction of particular incident

The position with time tag relevant to vehicle

With the special presupposition meaning event of time tag or the statistical calculations driving behavior data of not free sign

The vehicle dynamic data of special presupposition meaning event (for example velocity vector and vector acceleration)

Short-distance radio connects 320 and allows short range wireless data in information techenology unit 1000 and remote unit exchange, and for example, described remote unit is less than 500 meters, and usually, is less than 20 meters, away from described information techenology unit 1000.Can complete by a plurality of known short-distance radio schemes, for example described one or more:

The Bluetooth system of 2.4GHz bandwidth

2.4 and the wlan system of 5GHz bandwidth

The ISM bandwidth system of 433MHz, 866MHz, 315MHz, 915 MHz bandwidth, in communication process, usually, is used the agreement in limited liability cycle, and usually, original data transmissions speed is 200kbit/s to the maximum

UWB system within the scope of 3-10GHz

60GHz-24GHz communication system

24GHz communication system

60-80GHz radar system

24GHz radar system

Short-distance radio connects 320 and allows:

Be wirelessly connected to interior Vehicular system; Information techenology unit can comprise the internal information from Vehicular system, and for, for example, to time detecting with time tag and relevant measure and use it

The wireless connections of additional sensor, for example wireless camera connects or driving environment sensor

Be wirelessly connected to the personal computer device (PDA, smart mobile phone or suchlike) of chaufeur oneself

By himself, provide sensory activity, in order to add the aerial lug of feeler system by use, calculate distance or confirm target.

The connection of sensor 330 or regulation allow wired mode to be connected on special non-inertial sensor, are placed on the inner or described information techenology of described information techenology 1000 self 1000 self outside, for example sensor of environmental factor.

microphone 350 is for Video Capture

Being connected to the wireline interface of automotive system and annex 340 provides the wired mode of described information techenology unit 1000 to be connected to Vehicular system or annex by following at least one mode:

Vehicle OBD adaptor union

CAN interface

Linux interface

FlexRay interface

MOST interface

SPI interface

RS232 interface

USB interface

As described in Figure 2, described information techenology unit 1000 can lead to long-distance wireless network 3000 and be connected to teleprocessing entity 2000 or rear end, usually, is cell phone network.These parts form the system 4000 of another embodiment of the present invention together, will further discuss below.

Schematic diagram as shown in Figure 3, described information techenology unit 1000 can receive a large amount of input data and take many kinds of measures.Especially, in order to carry out a plurality of operations, described information techenology unit 1000 can receive input data, offers described processing and control element (PCE) 130 and described memory device 310, and described executable operations comprises following any or a plurality of:

From the position data of global position system, usually, by global location receiving system 110, provided

Inertance element data (for example acceleration/accel and velocity vector), usually, are provided by the 3D inertial sensor with 3D spinfunction 200

From the data of Vehicular system, information techenology unit is wherein installed, usually, by wireline interface 340, provided

By additional sensor (environment, annex) 330 data that provide

Control data (setting, program), usually, by rear end 2000, provided

Maintain and new data more, usually, by rear end 2000, provided

Data based on receiving, described processing and control element (PCE) 130 can be taked many kinds of measures, comprises as described below any one or more:

Calculate real time position data 11100

Calculate the vector route 11200 of real-time vehicle

Calculate the behavior of chaufeur and vehicle 11300

Detect 11400 computing time

After event occurs, calculate the vector route 11500 of vehicle

Alternatively, Preset Time warning is calculated and issued Vehicular system (chaufeur) 11600

Alternatively, realize and encrypting and Multimedia Compression 11700

Alternatively, initialization dependent event warning 11800

A core aspect of the present invention is that the input data of the 3D inertial sensor based on 3D spinfunction, detect and take risk and provoke the event 11400 of driving as feature, and therefore calculate driving behavior risk indicator 11300, will further introduce below.

As described in Figure 4, described inertial sensor 200 can detect vector acceleration " a ", and it has a size in the direction of vehicle acceleration.Especially, described acceleration has scalar key element a---in each three-dimensional cartesian coordinate system (X, Y, Z) _x, a--- _y, a- _z, it is measured by described inertia detector 200.In addition, described inertial sensor 200 can detect the angular acceleration of each axle, wherein α- _?, α- _θ, α- _Ψit is respectively the angular acceleration of axle X, Y, Z.Therefore, use described inertial sensor 200, described information techenology unit 1000 can be at default time detecting scalar acceleration information, and in identical cyclomorphosis acceleration.In addition, initial velocity known (being 0 before vehicle mobile), can calculate described speed (being all to change with speed scalar sum velocity vector), degrees of rotation, inclination and irrelevance.α- _?, α- _θ, α- _Ψrespectively the X, Y, Z axis data of described information techenology unit 1000 and vehicle.In addition, use the described inertance element that comprises 6 degree of freedom permission to determine the real-time vector route of vehicle, and the vehicle location of arbitrary time (degree that comprises rotation, tilts and depart from).

Correspondingly, by determining that described vehicle vector route, described scalar speed information, described scalar information, described velocity vector variation and described acceleration change, the dangerous driving behavior that can set up or represent unsafe event or occur.These events can comprise, as an example, unexpected or anxious accelerate or suddenly slow down, unsafe turning, for example understeer or ovdersteering, turn to suddenly, racing to, lane change fast, break away, keep away barrier, inordinate rotation, tilt and/or depart from, unsafe velocity variations and speed.

In the present embodiment, described Dian processing and control element (PCE) 130 receives data and moves a plurality of algorithms simultaneously from described inertance element 200, to detect each the event in a plurality of pre-set categories, calculate and use the quantity of event described in each classification, to set up driving behavior risk indicator.Table 1 is below an example of algorithm, and described example can be used by information techenology of the present invention unit.

Table 1

algorithm title	explanation
		anxious acceleration monitored	speed in measurement classifying vehicle longitudinal acceleration and Δ V(predetermined period changes in real time, is generally 30s)
sudden stop monitoring	measure in real time and classifying vehicle longitudinal acceleration and Δ V
		sharply turning monitoring	within the time cycle, to measure at a high speed and classifying vehicle transverse acceleration
oversteer monitoring	within the time cycle, to measure at a high speed and classifying vehicle transverse acceleration the migration velocity comparison under high speed with vehicle.Algorithm output can be further by being used different threshold test to break away, turn to racing to event suddenly
		hide monitoring	in short cycle, with high speed measuring vehicle quick steering (transverse acceleration).Algorithm output can be further by using the unexpected lane change event of different threshold test and keeping away one or two in barrier event
velocity variations monitoring	detect the velocity variations of (for example 1min, 2min) in preset time period.Important velocity variations is to provoke a feature of driving.Speed changes algorithm and detects and report the formerly velocity variations of predetermined threshold value
		speed monitoring	detect car speed in preset time period and whether exceed pre-set velocity scope.For example: vehicle more than 90km/h, travel 15min or the 10s that travels more than 160km/h
uninterrupted driving monitored	whether detect vehicle is uninterruptedly to travel in default distance.Whether for example-vehicle travels does not have the intermittence of minimum 15min for 4 hours above

Table 1 has illustrated each algorithm monitoring of a classification of event.Yet each classification can comprise a plurality of subclass, and except the event of monitoring primary categories, if needed, relevant algorithm can be monitored those subclass.For example, the described calculation vehicle monitor that the oversteer of sidesway is monitored rapidly.As the subclass of these events, the sideslip that described algorithm can monitoring vehicle, suddenly turn to or vehicle racing to.Each subclass can be in fact the same by using, but have the algorithm (as described in more detail below) of different threshold values to detect.Similarly, described calculation is hidden monitoring and is jointly detected the unexpected lane change event of described subclass and keep away barrier event, and wherein chaufeur can change fast direction and firmly brake to avoid bump shield hurdle.And each subclass can be in fact the same by using, but have the algorithm (as described in more detail below) of different threshold values to detect.

In addition, each classification of event (if or needing to detect the subclass of event) can be divided into " medium " and " serious " event, wherein, and matters of aggravation representative driving that more provoke or danger.And this can be by completing by a plurality of threshold values.Technical personnel will be experienced difference, and can use more classification.

In the testing process of event, " longitudinal acceleration " is defined as in specified time increment, is parallel to the acceleration unit of driving in direction.Therefore, if described vehicle is driven along the X-axis described in Fig. 4, Z axis is vertical, and described longitudinal acceleration is by the acceleration/accel being defined as in X-axis.Similarly, " transverse acceleration " is defined as in specified time increment, perpendicular to the acceleration unit in described driving direction.Similarly, the method for calculating of " departing from speed " is perpendicular to car plane, about " angular rate " in axle center or " cireular frequency (ω- _Ψ) "-in other words, if vehicle on X-Y plane, is Z axis." car speed " is defined as the moving velocity of vehicle.

Described information techenology unit 100 continues to extract input data from described inertance element 200 with monitoring event, and for example, such event can be monitored about 1 second.Sampling can complete between 10Hz and 100Hz, although the not restriction in the present invention of these sampling and event monitoring speed.

In more detail, Fig. 5 has illustrated the detection of described anxious accelerated events.First, from a plurality of default values of described memory device 310 retrieval.These are the numerical value of observation event window " event window 1 ", and usually, these numerical value are less than 1 second; The numerical value of acceleration rate threshold " acceleration rate threshold 1 ", usually, setting is greater than 0.2g, general numerical value setting or correction that wherein the numerical value of g=9.81m.s-2(" acceleration rate threshold 1 " also can be based on default, described default general numerical value depends on present speed value or external data for example weather condition or road conditions); Pull the numerical value of (derivative of acceleration/accel) threshold value " to pull suddenly threshold value 1 ", usually, be set to be greater than 0. 5 m.s suddenly ^-3; Acceleration rate threshold " acceleration rate threshold 2 " usually, be arranged on 0.2g following (general numerical value setting or correction that the numerical value of " acceleration rate threshold 2 " also can be based on default, described default general numerical value depends on present speed value or external data for example weather condition or road conditions); The difference numerical value of threshold speed " Δ threshold speed 1 ", usually, is set in 3 m.s ^-1(general numerical value setting or correction that " Δ threshold speed 1 " also can be based on default, described default general numerical value depends on present speed value or external data for example weather condition or road conditions) above.

In detecting described event, " average longitudinal acceleration " is defined as " longitudinal acceleration ", and each in the sample of the inertance element 200 from described is determined by described " watch window 1 " time, in the present embodiment, is less than 1s.

Described " average longitudinal acceleration " is stored in the cyclic buffer that a length mates with " watch window 1 ", and the numerical value of renewal is suitable for each sample, so the value storage of some " on average longitudinal accelerations " is in cyclic buffer.For example, if described sampling rate is 10Hz, described watch window is 1s, and every 0.1s calculates once the numerical value of " average longitudinal acceleration ", be based upon on the basis of last 10 samples, the value storage of 10 " average longitudinal accelerations " is in energy disperser." average longitudinal acceleration OLD " is the raw value from this cyclic buffer.

" pull suddenly " derivative as acceleration/accel, be defined as the difference of " average longitudinal acceleration " and " average longitudinal acceleration OLD " divided by " watch window 1 " the lasting time.

" possible acceleration/accel event " is logical variable or signal, and its initial condition (IC) is false.

Described algorithm is by showing that " average longitudinal acceleration " and " car speed ", from step S100, also upgrade the cyclic buffer that comprises " average longitudinal acceleration " numerical value.Also for this algorithm, the numerical value that is stored in " average longitudinal acceleration OLD " variable upgrades according to the sample in this energy disperser.Then, described algorithm enters its serviceability being represented by logical variable " possible acceleration/accel event ".In step S110, algorithm determines whether " possible acceleration/accel event " is true.

If " possible acceleration/accel event " is false, mean that vehicle is not anxious to accelerate operation, algorithm is by determining whether " average longitudinal acceleration " is greater than " acceleration rate threshold 1 " and checks the anxious first condition that accelerates to operate and whether meet step S210.Whether this condition mates with the algorithm that enters step S130.In step S130, the numerical value of " pulling suddenly " is defined as the difference of " average longitudinal acceleration " and " average longitudinal acceleration OLD " divided by the time length of " watch window 1 ".After this, process goes to S140, and whether the numerical value of this step judgement " pulling suddenly " is larger than " pulling suddenly threshold value 1 ".If Condition Matching, means and may start anxious acceleration operation, described " possible acceleration/accel event " signal is set to very in step S150, the real-time data memory of " car speed " is in variable " VELOCITY_INIT ".Then, the order of next step S101 is returned to and waited for to algorithm.If the condition of step S120 is not mated with algorithm, return to the also order of waiting step S101, and anxious accelerated events do not detected.If the condition of step S140 is not mated with algorithm, return to the also order of waiting step S101.

If " possible acceleration/accel event " is true, mean that vehicle is in the anxious operation of accelerating, whether the anxious acceleration operation described in algorithm judgement completes, and this process is by judging that whether " average longitudinal acceleration " be lower than " acceleration rate threshold 2 " in step S160.If this condition is mated with algorithm, enter step S170, in step S170, judge another condition, by judgement, implement " car speed " and whether be greater than " Δ threshold speed 1 " with the difference of the variable " VELOCITY_INIT " of storage.If Condition Matching, detects anxious pick-up time, algorithm enters step S180, and here, the concrete data of anxious pick-up time are stored in memory device 310.After this step, algorithm enters step S190, and here, " possible acceleration/accel event " is reset to vacation.Then algorithm is returned to the also Next Command of waiting step S101.If the difference in step S170 is less than " Δ threshold speed 1 ", algorithm skips to step S190, means and anxious accelerated events do not detected.If the condition of step S160 is not mated with algorithm, return to the also Next Command of waiting step S101.

Fig. 6 has illustrated detection sudden stop event.First, from memory device 310, retrieve a plurality of default values.These are the numerical value of observing event window " watch window 2 ", usually, are set to be less than 1s; The numerical value of acceleration rate threshold " brake threshold value 1 ", usually, is set to be greater than-0.4g(negative), wherein, 81 ms of g=9. ^-2(numerical value of " brake threshold value 1 " also can be according to general data setting or the correction of presupposition meaning, and presupposition meaning data dependence is in real-time speed or external data, as weather conditions or road conditions); " pull suddenly " threshold value of (derivative of acceleration/accel) " to pull suddenly threshold value 2 ", usually, be set to-0.4g(bears) below, (numerical value of " brake threshold value 2 " also can be according to general data setting or the correction of presupposition meaning, presupposition meaning data dependence is in real-time speed or external data, as weather conditions or road conditions); The difference of speed " Δ threshold speed 2 ", usually, is set to 3 ms ^-1(numerical value of " Δ threshold speed 2 " also can be according to general data setting or the correction of presupposition meaning, and presupposition meaning data dependence is in real-time speed or external data, as weather conditions or road conditions) above.

In the judgement of event, " average longitudinal acceleration " is defined as the time that " longitudinal acceleration " on average exceeds " watch window 2 ", in the present embodiment, is less than 1s.

" average longitudinal acceleration " is stored in the cyclic buffer mating with " watch window 2 "." average longitudinal acceleration OLD " is from the most original numerical value of cyclic buffer.

The derivative of acceleration/accel " pulls suddenly " and is defined as the difference of " average longitudinal acceleration " and " average longitudinal acceleration OLD " divided by the time length of " watch window 2 ".

" possible brake event " is logical variable or signal, and its initial condition (IC) is false.

Described algorithm is by showing that " average longitudinal acceleration " and " car speed ", from step S100, also upgrade the cyclic buffer that comprises " average longitudinal acceleration " numerical value.Also for this algorithm, the numerical value that is stored in " average longitudinal acceleration OLD " variable upgrades according to the sample in this energy disperser.Then, described algorithm enters its serviceability being represented by logical variable " possible brake event ".In step S210, algorithm determines whether " possible brake event " is true.

If " possible brake event " is false, mean that vehicle does not have sudden stop operation, whether algorithm is checked sudden stop operation first condition by determining whether " average longitudinal acceleration " is less than " brake threshold value 2 " meets step S220.Whether this condition mates with the algorithm that enters step S230.In step S230, the numerical value of " pulling suddenly " is defined as the difference of " average longitudinal acceleration " and " average longitudinal acceleration OLD " divided by the time length of " watch window 2 ".After this, process goes to S240, and whether the numerical value of this step judgement " pulling suddenly " little than " pulling suddenly threshold value 1 ".If Condition Matching, means and may start sudden stop operation, described " possible brake event " signal is set to very in step S250, and the real-time data memory of " car speed " is in variable " VELOCITY_INIT ".Then, the order of next step S201 is returned to and waited for to algorithm.If the condition of step S220 is not mated with algorithm, return to the also order of waiting step S201, and sudden stop event do not detected.If the condition of step S240 is not mated with algorithm, return to the also order of waiting step S201.

If " possible brake event " in step S210 is true, mean that vehicle is in the anxious operation of accelerating, whether described anxious of algorithm judgement accelerates operation and completes, and whether this process is by judgement " average longitudinal acceleration " on " the brake threshold value 2 " in step S260.If this condition is mated with algorithm, enter step S170, in step S170, judge another condition, by judgement, implement " car speed " and whether be greater than " Δ threshold speed 2 " with the difference of the variable " VELOCITY_INIT " of storage.If Condition Matching, detects anxious pick-up time, algorithm enters step S280, and here, the concrete data of sudden stop time are stored in memory device.After this step, algorithm enters step S290, and here, " possible brake event " is reset to vacation.Then algorithm is returned to the also Next Command of waiting step S201.If the difference in step S270 is less than " Δ threshold speed 2 ", algorithm skips to step S290, means and sudden stop event do not detected.If the condition of step S260 is not mated with algorithm, return to the also Next Command of waiting step S201.

Fig. 7 has illustrated detection sharply turning event.First, from memory device (not shown), retrieve a plurality of default values.These are the numerical value of observing event window " watch window 3 ", usually, are set to be less than 0.5s; The numerical value of acceleration rate threshold " brake threshold value 3 ", usually, is set to be greater than 0.4g(negative), wherein, 81 ms of g=9. ^-2(numerical value of " brake threshold value 3 " also can be according to general data setting or the correction of presupposition meaning, and presupposition meaning data dependence is in real-time speed or external data, as weather conditions or road conditions); The difference of threshold speed " threshold speed 1 ", usually, is set to be greater than 6 ms ^-1; Acceleration rate threshold " acceleration rate threshold 4 ", usually, the numerical value that is set to be less than 0.4g(" acceleration rate threshold 4 " also can be according to general data setting or the correction of presupposition meaning, and presupposition meaning data dependence is in real-time speed or external data, as weather conditions or road conditions).

In the judgement of event, " average lateral acceleration " is defined as the time that " transverse acceleration " on average exceeds " watch window 3 ", in the present embodiment, is less than 0.5s.

" possible sharply turning event " is logical variable or signal, and its initial condition (IC) is false.

Described algorithm is by showing that " average lateral acceleration " and " car speed " is from step S300, and then, described algorithm enters its serviceability being represented by logical variable " possible sharply turning event ", and initial condition (IC) is false.In step S310, algorithm determines whether " possible sharply turning event " is true.

If " possible sharply turning event " is false, mean that vehicle does not have sharply turning operation, algorithm is by determining whether " average lateral acceleration " is greater than " acceleration rate threshold 3 " and checks the first state that sharply turning operates and whether meet step S320.Whether this condition mates with the algorithm that enters step S330.In step S330, by judgement " car speed ", whether be greater than " threshold speed 1 " and determine second condition.If " Condition Matching, means and may start sharply turning operation, and " possible turning event " signal is set to very in step S340.Then, the order of next step S350 is returned to and waited for to algorithm.If the condition of step S320 is not mated with algorithm, return to the also order of waiting step S350, and sharply turning event do not detected.If the condition of step S330 is not mated with algorithm, return to the also order of waiting step S35550.

If " possible sharply turning event " is true, mean that vehicle is in sharply turning operation, whether the described sharply turning operation of algorithm judgement completes, and this process is by judging that whether " average lateral acceleration " be lower than " acceleration rate threshold 4 " in step S360.If this condition is mated with algorithm, enter step S370, here, the concrete data of sharply turning time are stored in memory device.Algorithm enters step S380, and here, " possible turning event " is reset to vacation.Then algorithm is returned to the also Next Command of waiting step S350.If the condition of step S360 is not mated with algorithm, return to the also Next Command of waiting step S350.

Fig. 8 has illustrated detection oversteer event.First, from memory device (not shown), retrieve a plurality of default values.These are the numerical value of observing event window " watch window 4 ", usually, are set to be less than 0.5s; The numerical value of acceleration rate threshold " acceleration rate threshold 5 ", usually, is set to be greater than 0.6g, wherein, and 81 ms of g=9. ^-2(numerical value of " acceleration rate threshold 5 " also can be according to general data setting or the correction of presupposition meaning, and presupposition meaning data dependence is in real-time speed or external data, as weather conditions or road conditions); The difference of acceleration/accel and " oversteer threshold value ", usually, the numerical value that is set to be greater than 0.2g(" oversteer threshold value " also can be according to general data setting or the correction of presupposition meaning, and presupposition meaning data dependence is in real-time speed or external data, as weather conditions or road conditions); The numerical value of acceleration rate threshold " acceleration rate threshold 6 ", usually, the numerical value that is set to be less than 0.4g(" acceleration rate threshold 6 " also can be according to general data setting or the correction of presupposition meaning, and presupposition meaning data dependence is in real-time speed or external data, as weather conditions or road conditions).

In the judgement of event, " average lateral acceleration " is defined as the time that " transverse acceleration " on average exceeds " watch window 4 ", in the present embodiment, is less than 0.5s.

" average departure speed " is defined as the time that " departing from speed " on average exceeds " watch window 4 ", in the present embodiment, is less than 0.5s.

" directed velocity calculating " is defined as the speed (that is to say the size of described velocity vector) travel direction fool, with the sampling rate of inertial sensor, estimates (if use these external datas, it is higher than calculating course or GNSS renewal speed).Therefore, if vehicle at longitudinal driving, it is car speed in the vertical that directed velocity is calculated, according to the sampling rate of sensor or the data estimation of sensor input of data.

" transverse acceleration calculating " is defined as the product of " average departure speed " and " directed velocity calculating ".

" possible oversteer event " is logical variable or signal, and its initial condition (IC) is false.

Described algorithm is by showing that " average lateral acceleration ", " average departure speed " and " car speed " are from step S400, and then, described algorithm enters its operating conditions being represented by logical variable " possible brake event ".In step S410, algorithm determines whether " possible oversteer event " is true.

If " possible oversteer event " is false, mean that vehicle does not have oversteer operation, algorithm is by determining whether " average lateral acceleration " is greater than " acceleration rate threshold 5 " and checks the first condition that oversteer operates and whether meet step S240.If this condition is mated with algorithm, enter step S430.In step S430, by determining whether " car speed " is greater than " car speed threshold value 2 " judgement second condition.If Condition Matching, means and may start oversteer operation, described " possible oversteer event " signal is set to very in step S440, and then, the order of next step S450 is returned to and waited for to algorithm.If the condition of step S420 is not mated with algorithm, return to the also order of waiting step S450.If the condition of step S430 is not mated with algorithm, return to the also order of waiting step S450.

If " possible oversteer event " in step S210 is true, mean that vehicle place may start oversteer operation, algorithm is calculated " transverse acceleration calculating " at step S460, and enter into step S470, here, with the absolute difference of " oversteer threshold value " comparison " transverse acceleration calculating " and " average lateral acceleration ".If described difference is greater than " oversteer threshold value ", the oversteer time in step S480, detected, and the concrete data of oversteer event are stored in memory device.After this step, algorithm enters step S490, and here, " possible oversteer event " is reset to vacation.Then algorithm is returned to the also Next Command of waiting step S450.If the difference in step S470 is less than " oversteer threshold value ", algorithm enters step S550, here judges, if " average lateral acceleration " is under " acceleration rate threshold 6 ", means and oversteer event do not detected, and algorithm enters step S490.Otherwise, if " average lateral acceleration " is greater than in step S500 " acceleration rate threshold 6 ", the oversteer time still likely being detected, algorithm is returned to the also Next Command of waiting step S450.

Fig. 7 has illustrated to detect and has hidden event.First, from memory device (not shown), retrieve a plurality of default values.These are the numerical value of observing event window " watch window 5 ", usually, are set to be less than 0.5s; The numerical value of acceleration rate threshold " acceleration rate threshold 7 ", usually, is set to be greater than 0.2g, wherein, and 81 ms of g=9. ^-2(numerical value of " acceleration rate threshold 7 " also can be according to general data setting or the correction of presupposition meaning, and presupposition meaning data dependence is in real-time speed or external data, as weather conditions or road conditions); The numerical value of threshold speed " threshold speed 3 ", usually, is set to be greater than 6 ms ^-1; Time threshold " time threshold 1 ", usually, the numerical value that is set to be less than 0.4s(" time threshold 1 " also can be according to general data setting or the correction of presupposition meaning, presupposition meaning data dependence is in real-time speed or external data, as weather conditions or road conditions); The numerical value of acceleration rate threshold " acceleration rate threshold 8 ", usually, the numerical value that is set to be greater than 0.3g(" acceleration rate threshold 8 " also can be according to general data setting or the correction of presupposition meaning, and presupposition meaning data dependence is in real-time speed or external data, as weather conditions or road conditions).

In the judgement of event, " average lateral acceleration " is defined as the time that " transverse acceleration " on average exceeds " watch window 5 ", in the present embodiment, is less than 0.5s.

" possible hide event " is logical variable or signal, and its initial condition (IC) is false.

" peak acceleration " is in hiding event, in order to store the variable of peak acceleration.

" minimum acceleration " is in hiding event, in order to store the variable of minimum acceleration.

" time counter " is to add up sample size and the variable of measuring and calculating time.

Described algorithm is by showing that " average lateral acceleration " and " car speed " is from step S600, and then, described algorithm enters its serviceability being represented by logical variable " possible hide event ", and its initial condition (IC) is vacation.In step S610, algorithm determines whether " possible hide event " is true.If " possible hide event " is false, mean that vehicle do not hide, whether algorithm is greater than " acceleration rate threshold 7 " by definite " average lateral acceleration " is checked the first condition of hiding operation and whether meets step S620.If this condition is mated with algorithm, enter step S630.In step S630, by judgement " car speed ", whether be greater than " threshold speed 2 " judgement second condition.If Condition Matching, mean to detect and hide event (in other words, may there is to hide event), in step S340, " possible hide event " is set to true, and " time counter " is set to 0, subsequently, in step S650, the real-time numerical value that " peak acceleration " and " minimum acceleration " arrives " average lateral acceleration " is set.Then, the order of next step S660 is returned to and waited for to algorithm.If the condition of step S620 or step S630 is not mated with algorithm, return to the also order of waiting step S660.

If " possible hide event " is true, mean to play and hide event, algorithm enters step S670,, increases the numerical value of " time counter " here.Enter subsequently step S680, in step S680, whether judgement " peak acceleration " is less than " average lateral acceleration ", if, algorithm enters step S690,, the real-time numerical value of " average lateral acceleration " is distributed to " peak acceleration " here, and described algorithm enters step S700.If the condition in step S680 is not mated with algorithm, directly enter into step S700.In step S700, whether judgment variable " minimum acceleration " is greater than " average lateral acceleration ", if, algorithm enters step S710, in step S710, the real-time numerical value of " average lateral acceleration " is distributed to " minimum acceleration ", and algorithm enters step S720.If the condition in S700 is not mated with algorithm, directly enter step S720.In step S720, whether judgement " time counter " is greater than " time threshold 1 ", and if so, algorithm enters step S730, otherwise, return to the also Next Command of waiting step S660.In step S730, judge whether two conditions mate, and that is to say, whether " peak acceleration " be larger than " acceleration rate threshold 8 ", and whether " minimum acceleration " be less than " acceleration rate threshold 8 ".If two Condition Matchings, mean to detect and hide event, algorithm enters S740, here, all concrete data of hiding event are all stored in memory device.After step S740, algorithm enters step S750, and here, " possible hide event " is reset to vacation.Algorithm is returned to the also Next Command of waiting step S660.If one or two condition in step S730 is not mated with algorithm, algorithm directly enters step S750.

Figure 10 has illustrated detection speed change events.First, from memory device (not shown), retrieve a plurality of default values.These are the numerical value of observing event window " watch window 6 ", usually, are set to be greater than 30s; The numerical value of velocity variations threshold value " velocity variations threshold value 1 ", usually, the numerical value that is set to be greater than 1(velocity variations threshold value " velocity variations threshold value 1 " also can be according to general data setting or the correction of presupposition meaning, presupposition meaning data dependence is in real-time speed or external data, as weather conditions or road conditions); The numerical value of time length threshold value " counter threshold 1 ", usually, is set to be greater than 10.

" car speed " be stored in mate with " watch window 6 " and the cyclic buffer with each sample continuous updating in.

In the judgement of event, " car speed variable " is defined as the variable that " car speed " is greater than default " watch window 6 " value, in the present embodiment, is greater than 30s.The standard mode that has multiple calculating variable, comprise absolute offset values, mean deviation amount, standard deviation amount etc., and any suitable variable method of measurement also can be used.For example, " car speed variable " can be defined as being retained in real time the poor of the maxim of " car speed " in energy disperser and minimum value simply.The result of maxim-minimum value, strictly speaking, is not variable, but +/-3 σ, variable can be σ ².

Described algorithm is by showing " car speed " and upgrading cyclic buffer from step S800, the historical data that the length of cyclic buffer by " watch window 6 " comprises " car speed ".Then, described algorithm enters step S810, and here, " car speed variable " is defined as the numerical value that " car speed " exceeds default watch window " watch window 6 ", and " watch window 6 " used the data of cyclic buffer.

After this, technique enters S820, and here, whether judgement " changes in vehicle speed " is greater than " stool change threshold 1 ".If this condition is mated with algorithm, enter step S830.In step S830, the numerical value of " counting machine " increases gradually.

After this, technique enters S840, here, and by judging whether the numerical value of " counting machine " is greater than " counter threshold 1 " and judges another condition.If Condition Matching, detects velocity variations event, algorithm enters step S850, and in step S850, the concrete data of speed event occurrence of variables are stored in memory device 310, and " counting machine " resets to 0.Algorithm is returned to the also Next Command of waiting step S801.

If the condition of step S820 is not mated with algorithm, enter into step S860, here, by judging whether the numerical value of " counting machine " is greater than another condition of 0 judgement.If so, described technique enters into step S870.In step S870, the numerical value of " counting machine " reduces 1, and then described technique proceeds to S840.If the condition of step S860 is not mated, described technique proceeds to S840.

Figure 11 understands the uninterrupted driving event that detects in more detail.First, from memory device (not shown), retrieve a plurality of default values.These are the numerical value of threshold speed " threshold speed 4 ", usually, are set to be less than 0 ms ^-1; The numerical value of time threshold " time threshold 2 ", usually, be set to be greater than 4h (numerical value of " time threshold 2 " also can be according to general data setting or the correction of presupposition meaning, and presupposition meaning data dependence is in real-time speed or external data, as weather conditions or road conditions); The numerical value of time threshold " time threshold 3 ", usually, the numerical value that is set to be greater than 15min(" time threshold 3 " also can be according to general data setting or the correction of presupposition meaning, and presupposition meaning data dependence is in real-time speed or external data, as weather conditions or road conditions).

In the judgement of event, " driving time counting machine " is the variable of measuring uninterrupted driving

" time count halted device " is the variable of measuring standing time.

" uninterruptedly driving " is for characterizing uninterrupted logical variable or the signal of driving of chaufeur, and its initial condition (IC) is false.

Described algorithm is by showing that " car speed " is from step S900.Then, described algorithm enters its serviceability being represented by the condition of S910, in step S910, determines whether " car speed " is greater than " car speed threshold value 4 ".

If the Condition Matching of S910, means vehicle under steam, algorithm enters step S920, and here, " driving time counting machine " increases, and in step S930, " time count halted device " is set to 0.After this step, be then step S940, here, whether judgement judgement " driving time counting machine " is greater than " time threshold 2 ".If Condition Matching, means that chaufeur does not stop the steering vehicle long period, algorithm enters step S950, and in step S950, " uninterruptedly driving " is set to true, and the order of next step S960 is returned to and waited for to algorithm.If the condition of step S940 is not mated with algorithm, return to the also order of waiting step S960.

If the condition of S910 is not mated, mean that vehicle is stable, algorithm enters step S970, and here, " time count halted device " increases.After this step, be step S980, here, whether judgement " time count halted device " is greater than " time threshold 3 ".If Condition Matching, means the chaufeur time enough of having had a rest, algorithm enters step S990.Whether judgement in step S990 " is uninterruptedly driven " is true.If submit coupling to, mean that chaufeur driven for a long time before having a rest, algorithm enters step S1000, and here, the concrete data of time are stored in memory device.After step S1000, in step S1010, variable " driving time counting machine " and " time count halted device " are all set to 0.Then, the order of next step S960 is returned to and waited for to algorithm.

If the condition of S910 is not mated, mean had a rest time enough and continue driving of chaufeur, algorithm enters step S1010, and here, " variable " driving time counting machine " and " time count halted device " are all set to 0.After this step, the order of next step S960 is returned to and waited for to algorithm.

The time explanation vehicle detecting is in the above described manner being driven.Each time may be relevant with the performance of timestamp and/or chaufeur, and timestamp represents the time of time detecting.In the present invention, detecting individual case in each classification is for determining the driving behavior risk indicator of chaufeur.Especially, described control and processing unit 130 calculate the time quantity of each classification always.In addition, storage weight factor or danger factor, preferably, the event of each classification is stored in look-up table, memory device 310.

As shown in figure 12, in order to calculate driving behavior risk indicator, concrete observing time or " dangerous cycle " are set.This can be, for example, one day or January, however arbitrary suitable cycle can be selected.The described cycle can be the cycle (for example, June 1 or whole September) in nearest cycle (for example, nearest 24 months, or nearest one month) or elective past.Described control and processing unit 130 retrieve all events in the risk cycle that occurring in memory device selected, together with the quantity of the time in the risk multiplier of look-up table and each classification relevant to risk multiplier.Then, the result that described control and processing unit 130 add these multipliers is to judge the accumulative risk in described risk cycle.

For a person skilled in the art, the danger of some events or risk behavior are larger than other, are apparent.For example, the danger of anxious acceleration or sudden stop is less than sharply turning and sudden turn of events road, and for example, these are dangerous less than sliding, rotate and keep away barrier conversely.Application risk multiplier is in order to express this, and therefore, for example, the described anxious risk multiplier accelerating is lower than what slide.Each parameter, for determining event separately, if determine that these events are placed on interesting risk indicator event on prior position, and is placed on other event on unessential position, and risk multiplier can be finely tuned as driving behavior risk indicator.Therefore,, if the risk multiplier numerical value of a classification in the time is 0, the event of that class will not calculated in driving behavior risk indicator.On the contrary, compare with other risk multiplier, the numerical value of driving behavior risk indicator is higher, and the effect in corresponding classification event is larger.

For example, if described control and processing unit 130 are suitable for monitoring anxious pick-up time, sharply turning time, coasting events and unexpected time to turn, so suitable weight (risk multiplier) can be, anxious accelerated events is 1, sharply turning event is between 1-5, coasting events is between 2-10, and turning to suddenly event is between 10-50.Here provide the scope of numerical value, in the scope of the independent risk multiplier of each classification also arranging those skilled in the art (or even exceeding described scope).In addition, if described event is divided into " moderate ", " serious " etc., different classes of event can be arranged to different risk multipliers.It will be recognized by one skilled in the art that these risk multipliers are only representational, can select arbitrary suitable risk multiplier.

The accumulative risk calculating in this way can be used as risk indicator, and does not need further correction.Yet in a preferred embodiment of the invention, described accumulative risk is the combination of described risk cycle time length, or divided by the time length in described risk cycle.Alternatively, this can be used as described risk indicator and does not need to revise further.

Yet, further, being preferably, the driving distance of risk in the cycle also recorded and stored by information techenology frame 1000.Then, final driving behavior risk indicator obtains by cutting apart the accumulative risk (or accumulative risk of time quantum) of described combination, and described combined accumulated risk is by measuring the driving distance measurement of risk in the cycle.If described risk indicator is not added up or cut apart by the lasting time in risk cycle, unadjusted accumulative risk can be cut apart by measuring driving distance, to obtain final driving behavior risk indicator.

Figure 13 has illustrated a specific embodiment, and wherein, the time of detection is anxious acceleration, sudden stop, sharply turning, sudden turn of events road, slide, keep away barrier, turn to suddenly, rotation, velocity variations and drive over the speed limit.

All need to can combination with time meter (not shown) from inertance element 200 by processing and control element (PCE) 130 inputs, to judge the data of driving behavior risk indicator, preferably, it is as a part for described processing and control element (PCE) 130.Further, described processing and control element (PCE) 130 can be determined and drive distance, linear velocity, cireular frequency, linear acceleration and angular acceleration according to the data of inertance element 200 inputs, also therefore, can determine the generation of above-mentioned each risk indicator time.Further, by using the described inertance element that contains 6 degree of freedom 200, can accurately detect the quantity of excessive risk time, for example, by detecting turning, the lane change can not be included in advance in Risk Calculation, slide, oversteer and understeer, keep away barrier, racing to and rotate, together with more low-risk " linearity " time, for example, Negotiation speed, velocity variations, acceleration/accel and brake monitoring.

As previously mentioned, in form 1 above, the six-freedom degree of this inertance element 200 (can comprise quantity and numerical value based on selected threshold, monitor severe turning, oversteer (sideslip), sharply turning and accumulation ground and/or vehicle rotation individually for monitoring severe horizontal event; And monitoring keeps away barrier event (can comprise quantity and numerical value based on selected threshold, monitor unexpected lane change and accumulation ground and/or keep away individually barrier).

In addition, in order to carry out the calculating of driving behavior risk indicator, do not need in the present invention the teleprocessing element of telematics unit 1000, this teleprocessing element can be removed if desired.Particularly, it is necessary only having inertial sensor 200, processing and control element (PCE) 130 and memory system 310, and in these, two or more can integrate.

Optionally, this unit 1000 utilizes other input to improve or adjust in addition the calculating of driving behavior risk indicator.For example, unit 1000 can utilize global location receiving system that automobile is placed on a pre-stored map, or via growing apart from transceiver 120, short-distance wireless connection 320 or wireline interface 340, from the map of teleprocessing entity 2000 or other resource downloading.This can be for relatively or correct track of vehicle or speed, and cartographic information, and the speed restriction of the segment path that for example vehicle travels, can be used for incoming event and survey.For example, the speed restriction that speed monitoring algorithm can be based on being derived from the cartographic information of the segment path that vehicle travels, compares car speed and threshold, and the generation of decision event on this basis.Global location data can also be for correcting or provide the distance of Vehicle Driving Cycle.In addition, rear end 2000 can be used global positioning system with the distance that monitoring vehicle was travelled, and this distance subsequently logical long-distance wireless network is sent to vehicle.

Although being described as, telematics unit 1000 surveys individual events and driving behavior risk indicator, but contrary, can there is the required secretary of event by judging whether in it, or the quantity of event sends to rear end 2000 in each classification, this rear end 2000 is carried out the incident detection of individual vehicle and/or the calculating of driving behavior risk indicator subsequently.Whether event count and/or driving behavior risk indicator are sent back to telematics unit 1000, or other equipment, no matter on vehicle.

Preferably, rear end 2000 comprises a server and/or processing entities, or a plurality of these equipment, and provide a web station interface or other interfaces, to allow long-distance user via website or other equipment, for example smart mobile phone, PDA etc., generate or obtain driving behavior risk indicator.

The connection of sensor 330 or regulation can allow the input of environmental conditions, for example temperature (for surveying possibility icing on road), rain, snow, high wind etc.As global location data, the input of sensor can be used for revising threshold and/or the risk multiplier for incident detection.Alternatively, based on environmental conditions, can revise the driving behavior risk indicator of calculating in normal way.For example, if chaufeur at high wind, snow, freeze and under with the event of symbol risk, drive in the rain, this has represented the risk higher than other times, and by one of method of just having discussed, or any other method, this has considered driving behavior risk index.If event and data are all with timestamp, event can meet the most general environmental conditions so, and one further improves driving behavior risk index.Like this, likely how foundation individual, vehicle, fleet tackle their risk under different weather and other environmental conditionss.Other possible inputs comprise vehicle input, for example brake and accelerator pedal angle, mileage meter and driving engine operation and On-Board Diagnostics (OBD) data.

Be worth appreciating, so far, in an independent vehicle, the telematics unit 1000 based on independent is described the calculating of driving behavior risk indicator, and generally speaking, is unit 1000 or vehicle calculating driving behavior risk indicator.Yet a car may be driven by different people, and should be individual and vehicle driving behavior risk indicator is provided.By being the risk cycle to be set for the first period obtain this, this first o'clock interim, a known specific chaufeur is just at steering vehicle, to calculate the first driving behavior risk indicator, and by being the risk cycle that arranges for the second period, be used for as other chaufeurs second calculate the second driving behavior risk indicator, interim at this second o'clock, known other chaufeurs are just at steering vehicle.Optionally, can pass through the chaufeur of other required identifier marking individualities of independent ignition switch or operation vehicle.Independent event is calculated, can be automatically for each chaufeur produces operating range and driving behavior risk indicator.

In another embodiment, the present invention can be used for judging vehicle in fleet and/or the driving behavior risk indicator of chaufeur and individual vehicle.

Therefore, Fig. 4 shows according to system 5000 of the present invention, and this system comprises a rear end 2000 and a plurality of teleprocessing frame 1000, and each this teleprocessing frame 1000 carries out communication via long apart from wireless network 3000 and rear end 2000.In a preferred embodiment, control and processing unit 130 calculate the event of each classification, and counting is stored in memory system 310.Each processing unit is subsequently with the number of times preset and default frequency, or the request based on rear end 2000, and counting is sent to back-end processing unit 2000.Preferably, event is sent and is gone back together with the timestamp with there is event, and can together with their institutes from the indication of telematics unit 1000 and/or event while occurring the indication transmission of chaufeur go back.Optionally, if event is sent to rear end 2000 with conventional frequency, or do not need to calculate the posture behavior risk indicator of particular risk period and/or particular vehicle and/or chaufeur, just do not need the indication of the indication/chaufeur of timestamp/vehicle.

As shown in figure 15, rear end 2000 is calculated the summation of all events of each classification subsequently, and risk multiplier is applicable to the event number in related category.Figure 16 shows a specific embodiment, and in this embodiment, the event after detection is violent step on the accelerator, sudden stop, sharply turning, urgent lane change, slides, keeps away barrier, sharply turning, rotation, velocity variations and hypervelocity.

Can also be by for adjusting risk multiplier (and or not being the threshold of using at event monitoring) if other are input to telematics unit 1000, these input data send to rear end 2000, still preferably together with timestamp, and risk multiplier is adjusted in rear end, because before the summation of the risk multiplier quantity of event, they are applicable to the event of being surveyed by different telematics unit 1000 in calculating any classification.In fact, can create the secondary classification with different risk multipliers for summation enters accumulative total risk.

By obtaining the counting of all events of each classification in fleet, risk multiplier is applicable to the counting of related category, and result is applicable to obtain accumulative total risk, likely judge generally the posture behavior risk indicator of fleet.

As mentioned above, accumulative total risk can risk period duration integrate, or by risk period duration divide.

For the calculating of the driving behavior risk indicator of fleet, the total distance that can travel by fleet is divided the accumulative total risk (no matter whether being to integrate or each time quantum the time) of fleet.By calculating the summation of the distance that each telematics unit of fleet sends, or can obtain this total distance by utilizing GPS or other global positioning systems to follow the tracks of each car, determine by this way the distance that in fleet, each car travels and the summation of calculating operating range, total distance of travelling to obtain fleet.

If telematics unit is not so done, the individual driving behavior risk of each car in fleet and/or each chaufeur can also be calculated in rear end 2000.It should be noted that rear end 2000 can calculate the driving behavior risk indicator of the accumulation of chaufeur, even if drive different vehicles in Ta fleet.For example, based on running route or number of times, the driving behavior risk of the subset of chaufeur in fleet and/or vehicle can also be calculated in rear end 2000.

Although system has been described as each telematics unit 1000 and has surveyed individual events and they are sent to rear end 2000, but not equal to some or all of telematics units can replace sending to rear end 2000 by the required input message of detection event, incident detection except calculating the driving behavior risk indicator of individual vehicle and/or whole fleet, is also carried out subsequently in this rear end.

Therefore, the invention provides independent entity:

This unit of unit 1000(can have but not be to have teleprocessing function);

A rear end 2000; And

A system 4000,5000 that comprises rear end 2000 and one or more unit 1000.

Wherein each can both calculate one or more driving behavior risk indicators of single vehicle, the index of the index He Yige fleet of single chaufeur.In addition, such index is applicable to specific period (risk period).For example, can calculate:

The risk indicator that daily index-1 of a chaufeur chaufeur (1 car) calculated in 1 day.

The risk indicator that monthly score-1 of a chaufeur chaufeur (1 car) calculated in 1 month.

The risk indicator that the fleet of the daily index-N>1 of a fleet car was calculated in 1 day.

The risk indicator that the fleet of the monthly score-N>1 of a fleet car was calculated in 1 month.

Certainly, if data are kept to ground long enough, specific risk can be used in and provide an index of driving safest time and season period, and the index of incidental event type when, so as can by driver training in the high risk time, more safely drive.

It should be noted that, although be preferably, it is not " fleet " of the present invention necessary feature in aspect that but telematics unit 1000 comprises the six-freedom degree of an inertance element 200, and this inertance element comprises the 3D inertial sensor with 3D spinfunction.Particularly, for example, can utilize one not have the 3D accelerometer of spinfunction to put into practice this aspect of the present invention.

The driving behavior risk indicator that motor insurance company can advantageously utilize the present invention to calculate, to eliminate the mutual risk of a car of driver driving of the formed risk of individual driver, appointment, and driver's risk in fleet and individual vehicle and fleet.In addition, underwriter and fleet everyone and operator can utilize this driving behavior risk indicator, to set up which car or any vehicle quality, it is safe driving, and which and path be safest time, and who chaufeur should be picked out separately the situation worst of carrying out extra safety training or training activity.Driving behavior risk indicator is also relevant with individual, family, vehicle manufacturer and government department.

Only provide by way of example description above, and skilled in the art will recognize that in the situation that not departing from the scope of the invention and can make correction.

Particularly, explained violent step on the accelerator, sudden stop, sharply turning, oversteer, the detailed algorithm of evading manipulation, velocity variations and fatigue driving, each algorithm itself is considered to innovation.Yet, it is to be further understood that the variant of each algorithm also may be still within the scope of the present invention.

For example, likely remove car speed limit test, or in arbitrary or whole above-mentioned algorithm usage flag/Boolean variable not.In addition, do not use aviation value (for example average longitudinal acceleration), can use other method of measurement, for example maxim and intermediate value.

In another embodiment, the present invention is that the reconstruction of vehicle path is prepared, particularly, but not exclusively in the situation that of vehicle collision.

For example, it is well-known using the collision detection of accelerometer, and, the in the situation that of this detection of needs, can use any known method.In the present invention, preferably, with reference to Figure 17 and 18, as below describing ground, carry out the detection of collision accident.

Particularly, serious and not serious collision accident are distinguishing.Figure 17 shows the detection of not serious collision accident, and this detection is based on the monitoring between short-term window phase, velocity vector being changed.During default time window, acceleration continues accumulation.Meanwhile, in horizontal and vertical face, algorithm has calculated the main direction (PDOF) of power.PDOF has determined the value of normalization factor, and this value is for making the variation normalisation of velocity vector.Particularly, normalisation factor is the function of PDOF.When this normalisation variation of velocity vector has surpassed the threshold (all inputs are all normal) that is preset as 1, detect common together collision, and the PDOF after calculating is recorded as " collision PDOF ".This has caused the cumulative process of variation and the beginning of time meter of velocity vector, to determine the time length of collision.The short-term summation of acceleration is continued until that it finishes threshold lower than default collision, the end of collision accident that this collision has finished threshold mark.If collided interim, the accumulative total of velocity vector changes lower than the serious determined threshold of collision accident, and it is not serious that this collision is automatically considered to.If equipment is surveyed the collision of a plurality of collisions or a upset, or another indication that has passenger to be detained, the final variation of speed has increased, and again compares with threshold.

Same, Figure 18 shows the detection of matters of aggravation, and this detection is based on the monitoring between short-term window phase, velocity vector being changed.During default time window, acceleration continues accumulation.Meanwhile, in horizontal and vertical face, algorithm has calculated the power (PDOF) of main direction on horizontal plane and vertical plane surface.Again, PDOF has determined the numerical value of criteria factor, and this numerical value is for normalisation velocity vector.Sometime, when the velocity vector that this normalisation changes is worked as all inputs all by normalisation over a threshold value to numerical value 1(), a common collision detected, and the PDOF calculating also can be registered as " collision PDOF ".This impels by the process that velocity vector accelerate to change and together with the initial point of time meter, decides the time length of this collision.The acceleration/accel of a short-term integrated refer to and lasted till that it drops on below default collision threshold, this threshold marker the end point of collision accident.If collided interim, the accumulative total of velocity vector changes lower than the serious determined threshold of collision accident, and it is not serious that this collision is automatically considered to.If equipment is surveyed the collision of a plurality of collisions or a upset, or another indication that has passenger to be detained, the final variation of speed has increased, and again compares with threshold.After this, to thering is the ratio of the serious collision of one medium (25-75%) and high (being greater than 75%) ratio, carry out an extra layering this collision is classified again.

In the present embodiment, if if determine and serious accident to have occurred or had a sufficiently high probability that such event occurs, can carry out the reconstruction of track of vehicle.Yet track reconstructing can and be feasible by the track reconstructing of description in any enforcement of required time below.

Be typical collision time line as shown in figure 19, wherein collision accident is separated into four different periods:

Interval 0 between T-1 and T0, it can be defined as one group of time, for example, in collision accident, occur first 10 seconds;

Interval 1 between T0 and T1, wherein, when collision starts, huge power is applied on vehicle at once,, conventionally continue 250 milliseconds;

Interval 2 between T1 and T2, following closely, and between the time point that the initial point in collision finally finishes to it, less masterpiece is used on vehicle, conventionally continues approximately 10 seconds; And

Interval 3 between T2 and T3, it can be defined as the period of one group, for example, from 10 seconds to 10 minutes, wherein, vehicle after collision in dead position.Use the long duration for interval 3, as described below.In a preferred embodiment, be confirmed as interval 3 at a stable state (motionless), adding the time limit of a default time when starting to detect vehicle.

While using acceleration pick-up and dead reckoning system, tend to the output of drift.Particularly, because any system is used, be to add to previous calculating location or output speed, cireular frequency, acceleration/accel and angular acceleration by constantly increasing the variation detecting.Any measured error, although very little, from point-to-point, accumulated.This has caused " drift ", or system think position and actual position between ever-increasing difference.

In addition, a rate gyroscope biasing, or biased error, when gyroscope any rotation can not occur, be from gyro output signal.Even the most perfect gyroscope also has error source and biasing is exactly one of them error.Biasing can be expressed as the percentum of a voltage or full amount output, but it is a rotative speed (degree/second) in essence.Unfortunately, biased error is tending towards changing, all relevant with temperature and time.A gyrostatic inclined to one side error is due to some assemblies: calibration error; Switch connection; Offset drift; With the effect of clashing into, this may be because collision becomes more firm.The individual measurement of biasing is also affected by noise, so the measurement of a significant deviation can be by the average measurement of a serial measurement.In addition, suppose that every other factor is constant, biasing may time to time change.

Therefore,, shown in Figure 20, in vehicle normal course of operation, the sensor error model of regular update, for estimating output error and correction of deviation and the drift in inertia sensing unit, comprises the three-dimensional gyroscope function of 3D inertial sensor.

In first box, the output that comes from inertial sensor unit 200 is stored in a cyclic buffer of default sampling frequency.In each upgrades, be whether whether variation by inquiry accelerometer be greater than default " ACC stable threshold " and decide automobile to move.If vehicle is motionless, the stabilized conditions of this vehicle, is to get used to upgrading sensor error model by a zero velocity renewal technology.Then this algorithm turns back to beginning, and is waiting for the next data sample from inertial sensor unit.

In addition, if being optional mensuration mobile or that whether vehicle is moving, vehicle is omitted, previously definite parameter was for compensating this inertial sensor data collection, this sensor data set is used the error model of current sensor and the data set after compensation, then for calculating Che Zhuan Tai – position and the attitude of prediction, pitching and Pian Hang – scalar speed information, scalar acceleration information, the variation of velocity vector, acceleration variation etc.Particularly, the error model of sensor, comprises that the various parameter/variablees of a large amount of use come the mathematical algorithm of compensated acceleration meter deviation, the scale factor of accelerometer, accelerometer gyroscope biasing, center cross compensation, gyro scale factor, gyro wander, if (provided, the speed proportional factor of mileage meter, magnetometer scale factor, magnetometer biasing) etc.

In determining frame, check that to be whether data from the data set of external sensor receive.In general, such external data will receive position data by GPS receiver 110, but also can comprise other data, as the three-axis attitude data from external sensor, for example, when vehicle is static.If do not receive external data, this system will not continue in the situation that there is no correcting sensor error model.

Yet if received external data, this is to compare with the vehicle-state of prediction.For example, if satellite location data is received in the time gap between 0.1 second and 1 second, the difference that this telematics unit 1000 calculates between predicted position, this predicted position is the output of proofreading and correct of the unit based on from inertial sensor and the position that provided by satellite location data.Equally, in the attitude of prediction and the difference between extra attitude data, also can be determined.This difference is called as " innovation " at Figure 20.

Subsequently, the variable that is somebody's turn to do " innovation " is for upgrading the parameter of sensor error model, this sensor error model tuning sensor bias, ratio factor parameter, gyro scale factor, gyrostatic biasing, accumulative total drift etc.Particularly, linearity or nonlinear estimation (may comprise any Kalman filtering, extended Kalman filter (EKFs), particle filter, Kalman filter and invisible (unscended) Kalman filter) be that variable based on " innovation " upgrades sensor error model.

The sensor error model of this renewal, subsequently for upgrading the prediction state of vehicle and process until the next sample of next cycle.

The error model of a plurality of sensors is stored in cyclic buffer, a plurality of by regular update, according to every 0.1 second once.

Be the mensuration of collision track reconstructing of the present invention as shown in figure 21.Before reconstruction starts, the interval 3 in Figure 19 is mistake, if for example there is no or only have very little exporting change in inertial sensor unit 200, more preferably, default time out phase.Then, inertial sensor at interval t0 is to be stored in time gap T3 for compensation, inertial data collection is stored in the cyclic buffer of time gap T3, and all inertial data collection of the sensor error of self compensation in the future model are stored between T0 and the interrecord gap of T3.Gyro wander compensation can further improve stable state.Particularly, it likely determines time T 0 and retrieval and application sensors error model when starting to collide.The error model of this sensor, will be still effective, because self collision starts to only have going by seldom, but the unexpected impact of the acceleration/accel that model can not received in collision process.

Subsequently, this average GNSS position (position that satellite is definite), the average final heading of average acceleration vector was calculated in interval 3.Finally, the attitude of the stationary vehicle of vehicle is also determined to (may comprise last driftage and final roller and final spacing) from the data set of inertial sensor of compensation.

As shown in Figure 20, this final vehicle-state is known in time T 3.This can be by determine the GNSS data of the vehicle of (or another kind of GNSS satellite navigation system) data or average last dead position from outside GNSS, or other are externally measured.It is preferred outside determining, rolling accurately, the possibility that pitching and driftage are measured of obtaining also.

Based on this outside final vehicle-state of determining, the GNSS position that this is average, average acceleration vector, average final heading and final rolling and final spacing, and inertial sensor data collection is used the correction of sensor error model in time T 0, may be by determining that Che Zhuan Tai – position and posture reconstruction track of vehicle three dimensions (roll, pitching and rate of yaw information, therefrom optional) scalar, scalar acceleration information, the variation of velocity vector, the variation of acceleration etc.Vehicle-state can be calculated, and compensates inertial sensor data collection be stored in cyclic buffer for each, at moment t2, starts by interval 2 to moment T1, to work backward, and interval 1, interval 0 was to moment t-1 to moment t0.Particularly, vehicle-state can turn to example by solving equation and acceleration one and utilize direction cosine, and Eulerian angles are determined, quaternion and/or axial vector.

Because the position of final stationary vehicle is known, the state of vehicle can be matched on the specific position on the road occurring at them exactly, and can determine a forensic analysis accurately.The state that in addition, can send the vehicle after calculating is to user and the reconstruction (position, velocity vector, attitude) of three dimensions of the path of motion of the vehicle before collision is provided.

As previously mentioned, the cyclic buffer that telematics unit 1000 is constantly updated with the data set of inertial sensor.As shown in figure 22 for a kind of for recording the typical system of inertial sensor data collection.If collision detected, this sampling frequency can be enhanced or strengthen after time T 0 or after other moment T0.In addition, after sampling, T3 can standing time.

In addition, the track reconstructing of the teleprocessing function of unit 1000 is optional, and related data can be passed through Wi-Fi, the accesses such as USB interface for investigator or other users.Yet, if the teleprocessing function providing, in collision process or afterwards, any and all data about track reconstructing that this unit 1000 can send are automatically to the teleprocessing entity (data set that comprises inertial sensor, the error model of sensor, and track calculates).This unit can also be configured for these data of storage under the storage of a special safety, and this storage is the impact that is not easy to be collided and destruction.

Track may be in unit 1000 be rebuild, this teleprocessing entity 2000 or another processing equipment, and a notebook PC for example, from unit, 1000 retrieve necessary information by wireless or wireline interface.

Providing by way of example of foregoing description, is to be understood that it can make correction by those skilled in the art, and does not depart from the scope of the present invention.

Claims

1. an equipment that calculates vehicle driver's driving behavior risk indicator, comprising:

One processing and control element (PCE); And

One memory device;

This equipment is applicable to:

Obtain an event count, described event count is based on coming from the input that is arranged on the inertance element on vehicle, occur in each in a plurality of pre-set categories, described inertance element comprises the 3D inertial sensor with 3D spinfunction, and each representations of events is dangerous driving and provocation driving at least one times; And

The quantity of the event based on each classification is calculated driving behavior risk indicator.

2. equipment according to claim 1, is stored in the weight factor separately of corresponding each classification in memory device, and described equipment calculates driving behavior risk indicator by corresponding weight factor being applied to each classification event number.

3. equipment according to claim 1 and 2, the event number based on occurring in predetermined period, calculates driving behavior risk indicator.

4. equipment according to claim 3, the time length based on predetermined period, calculates driving behavior Risk Calculation.

5. according to the equipment described in claim 3 or 4, the driving distance based on predetermined period, calculates driving behavior risk indicator.

6. equipment according to claim 1, based on following reason, calculates driving behavior risk indicator:

Acquisition occurs in the event number of the predetermined period in each classification;

By the event weights factor addition of all categories with to obtain the accumulative risk of predetermined period;

Determine that vehicle is in the driving distance of predetermined period; And

By driving distance, distinguish accumulative risk.

7. equipment according to claim 6, the time based on lasting, revises the accumulative risk of predetermined period.

8. according to the equipment described in aforementioned arbitrary claim, based on environmental data, revise driving behavior risk indicator.

9. equipment according to claim 8, described environmental data comprises road condition data, temperature data, at least one in weather data and geographic position data around.

10. according to the equipment described in aforementioned arbitrary claim, pre-set categories comprises sharply turning, oversteer and evades two or more arbitrarily in manipulation.

11. according to the equipment described in aforementioned arbitrary claim, and described equipment can be arranged in vehicle.

12. equipment according to claim 11, comprise described inertia unit.

13. according to the equipment described in claim 11 or 12, further, also comprises projector and receptor for being communicated with teleprocessing entity.

14. according to arbitrary equipment of claim 1-10,

Equipment described in remote control on vehicle;

On vehicle, detect the event of each classification;

The data of relevant each event, and

The event number of each classification.

15. according to the arbitrary equipment described in claim 1-10, described equipment

Remote control on vehicle; And

By accepting and process the data of the inertance element input being arranged on vehicle, obtain described quantity.

16. according to the equipment described in claims 14 or 15, is suitable for obtaining the event number of each vehicle of a plurality of different vehicle kinds in fleet, and described event occurs in each in a plurality of pre-set categories, and the driving behavior risk indicator of definite fleet.

17. equipment according to claim 16, further, are suitable for fleet's driving behavior risk indicator and alternatively, and at least one in the relative driving behavior risk indicator of acquisition be single car driving behavior risk indicator relatively.

18. equipment according to claim 16, further, alternatively, the relative driving behavior risk indicator of acquisition is single car driving behavior risk indicator relatively.

19. according to the arbitrary system that comprises processing entities and a plurality of equipment in claim 11-13, and the wireless network of described processing entities dependence one long scope is communicated with each in a plurality of equipment.

20. comprise the system of a plurality of information techenologys unit and remote processing unit, and described information techenology cellular installation, on corresponding vehicle, is characterized in that:

Described information techenology unit comprises inertial sensor unit;

21. systems according to claim 20, described inertial sensor unit comprises the 3D inertial sensor with 3D spinfunction.

22. according to the system described in claim 20 or 21, and at least one teleprocessing entity and each information techenology unit are suitable for calculating driving behavior risk indicator.

23. 1 kinds of methods of calculating vehicle driver's driving behavior risk indicator, comprising:

24. methods according to claim 23, comprise further, by the event number weight of each classification being applied in classification separately, calculate driving behavior risk indicator.

25. according to the method described in claim 23 or 24, further, comprises that the event number based on occurring in predetermined period is calculated driving behavior risk indicator.

26. methods according to claim 25, further, comprise, the time length based on predetermined period calculates driving behavior risk indicator.

27. according to the method described in claim 25 or 26, further, comprises that the driving distance based on predetermined period is calculated driving behavior risk indicator.

28. methods according to claim 23, comprise by following calculating driving behavior risk indicator:

The quantity of acquisition event, described event occurs in the predetermined period of each classification;

Corresponding weight factor is applied to the quantity of event, described event occurs in the predetermined period of each classification;

The weighted number of all categories event is added and to obtain the accumulative risk of predetermined period;

Determine that vehicle is in the driving distance of predetermined period; And

By driving distance, distinguish accumulative risk.

29. methods according to claim 28, further, comprise, the described time length based on the described cycle, the accumulative risk of correction predetermined period.

30. according to the arbitrary described method in claim 23-29, further, comprises the driving behavior risk indicator based on described in environmental data correction.

31. methods according to claim 30, described environmental data comprises road condition data, temperature data, at least one in weather data and geographic position data around.

32. according to the arbitrary described method in claim 23-31, and described pre-set categories comprises sharply turning, oversteer and evades two or more arbitrarily in manipulation.

33. according to the arbitrary described method in claim 23-32,

On vehicle, detect the event of each classification; And

Described method comprises further, by receiving following at least one quantity of vehicle:

About the data of each event, and

Event number in each classification.

34. according to the arbitrary described method in claim 23-32, comprises by receiving and process quantity described in the input data acquisition of the inertance element being arranged on vehicle.

35. according to the method described in claim 33 or 34, the event number that comprises each vehicle that obtains a plurality of different vehicle kinds in fleet, and described event occurs in each in a plurality of pre-set categories, and the driving behavior risk indicator of definite fleet.

36. methods according to claim 35, further, comprise with the driving behavior risk indicator of fleet and alternatively, obtain at least one the comparison single car driving behavior risk indicator in relative driving behavior risk indicator.

37. methods according to claim 35, further, comprise with alternatively, obtain relatively single car driving behavior risk indicator of relative driving behavior risk indicator.

38. 1 kinds of methods of determining a plurality of vehicular drive behavior risk indicators, described method comprises:

Based on being arranged on the inertance element on vehicle described in each, detect the event in a plurality of pre-set categories that occurs in, each event represents dangerous driving and provokes at least one of driving; And

Event number based on each classification, calculates the driving behavior risk indicator of a plurality of vehicles.

39. according to the method described in claim 38, and described inertial sensor unit comprises the 3D inertial sensor with 3D spinfunction.

40. 1 kinds of methods of again planning the route of vehicle, comprising:

In the first Preset Time storage data, each storage data is included in corresponding the first storage life, the inertance element output data on being arranged on vehicle, and described inertance element comprises the 3D inertial sensor with 3D spinfunction;

Detection event;

After described event starts, based on before described event starts in the recent period, the sensor error model of storage is stored in after described event starts, upgrade and start to store each data setting to the 3rd Preset Time from described event; And

Data based on upgrading, plan vehicle route again.

41. according to the method described in claim 40, it is characterized in that, described event is car accident.

42. according to the method described in claim 40 or 41, it is characterized in that the one-period between the fixed cycle that the 3rd described predeterminable event is defined as described event after starting and the fixed cycle changing from output signal inertance element, under predetermined period.

43. according to the arbitrary described method of claim 40-42, it is characterized in that, after detecting described event, adjusts and is stored in the wave band in the data of the first Preset Time.

44. according to the arbitrary described method of claim 40-43, comprises further, based on external data, determines the dead position of vehicle after event, it is characterized in that, the described circuit of again planning is the data based on final position is upgraded as initial point.

45. according to the method described in claim 44, it is characterized in that, described dead position data comprise the posture of vehicle and at least one data in satellite location data.

46. according to the either method described in claim 40-45, further, comprising:

The mean place that measure and calculation goes out, the average acceleration calculating and average finally towards at least one, described position is after detecting described event, uses the data that are stored in the renewal between the fixed cycle;

Based on measurement result again programme path.

47. according to the either method described in claim 40-46, further, comprising:

Based on measurement result again programme path.

48. according to the either method described in claim 40-47, it is characterized in that, described programme path again comprises at least one in vehicle location, speed and the posture of using the storage data of upgrading to calculate multiple the first predetermined period.

49. according to the either method described in claim 40-48, further, before being included in the setting of the described sensor error model of renewal, calculates the inertial sensor data of upgrading.

50. 1 kinds of equipment that implement the claims the method described in 40-49.

51. 1 kinds of equipment that use in again planning vehicle route, described equipment comprises:

Processing and control element (PCE); And

Memory device,

Described equipment is suitable:

Detection event; And

Storage sensor error model based on being stored in the major part nearest information of described event before starting, after event starts, upgrades and starts each the data setting to the 3rd Preset Time from described event; And.

52. according to the equipment described in claim 51, is more suitable for the reconstruction of two tracks of getting on the bus on Data Update basis.

53. according to the equipment described in claim 51 or 52, it is characterized in that, described event is car accident.

54. according to the arbitrary equipment described in claim 51-53, it is characterized in that, the 3rd described Preset Time be defined as fixed cycle after one of them event occurs and from the signal of inertance element output still lower than fixed cycle of preset value.

55. according to the arbitrary equipment described in claim 51 to 54, it is characterized in that, after detecting described event, adjusts and be stored in the wave band in the data of the first Preset Time.

56. according to the arbitrary equipment described in claim 51-55, further, is suitable for based on external data, after event occurs, determining the dead position data of vehicle, and the data based on upgrading arrange programme path again, using definite final position as starting point.

57. according to the arbitrary equipment described in claim 51-56, it is characterized in that, described stationary vehicle position data comprises the posture of vehicle and at least one in satellite location data.

58. according to the arbitrary equipment described in claim 51-57, further, is suitable for:

Data based on upgrading, plan vehicle route again.

59. according to the arbitrary equipment described in claim 51-58, further, is suitable for:

Based on measurement result again programme path.

60. according to the arbitrary equipment described in claim 51-59, is suitable for multiple the first predetermined period, after the storage data setting of use upgrading, by calculating at least one programme path again in vehicle location, speed and posture.

61. according to the arbitrary equipment described in claim 51-60, further, is suitable for, before upgrading described sensor error model setting, calculating the inertial data setting of upgrading.